| Title:
|
A novel robust principal component analysis method for image and video processing (English) |
| Author:
|
Huan, Guoqiang |
| Author:
|
Li, Ying |
| Author:
|
Song, Zhanjie |
| Language:
|
English |
| Journal:
|
Applications of Mathematics |
| ISSN:
|
0862-7940 (print) |
| ISSN:
|
1572-9109 (online) |
| Volume:
|
61 |
| Issue:
|
2 |
| Year:
|
2016 |
| Pages:
|
197-214 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the $\ell _1$-norm. However, the sparse noise has clustering effect in practice so using a certain $\ell _p$-norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery and contiguous outliers detection, by enforcing the low-rank constraint in a matrix factorization formulation and incorporating the contiguity prior as a sparsity constraint. The experiments on both synthetic data and some practical computer vision applications show that the novel method proposed in this paper is competitive when compared with other state-of-the-art methods. (English) |
| Keyword:
|
robust principal component analysis |
| Keyword:
|
sparse Bayesian learning |
| Keyword:
|
Markov random fields |
| Keyword:
|
matrix factorization |
| Keyword:
|
contiguity prior |
| MSC:
|
60J20 |
| MSC:
|
62H25 |
| MSC:
|
68Q87 |
| idZBL:
|
Zbl 06562153 |
| idMR:
|
MR3470773 |
| DOI:
|
10.1007/s10492-016-0128-8 |
| . |
| Date available:
|
2016-03-17T19:34:58Z |
| Last updated:
|
2020-07-02 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/144844 |
| . |
| Reference:
|
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